Ph.D. Research Talk: Optimizing Product Yield Through Identifying Gene Expression Fold Changes

Abstract

Microbial cells have been successfully engineered to produce a large variety of biomolecules useful as biofuels, drugs and drug- precursors. One challenge in maximizing the production of a target metabolite within a microbial cell is identifying gene modifications in the form of up-regulation, down-regulation, or knockout. Several computational tools have been developed for strain optimization. Importantly, solutions to strain optimization problems must respect new bounds imposed due to regulation modification. As a result, new steady-state constraints are imposed, referred to as dynamic constraints, on the system as a whole. Another challenge in strain optimization is identifying an optimal fold change (e.g., 2x, 5x, or 10x), instead of just identifying the fold change direction (up or down regulation). Prior computational approaches do not consider updating the steady-state boundaries nor identify gene fold modification.

We propose a new strain optimization formulation that identifies fold changes required to maximize cellular yield. Simulated Annealing (SA) is used to identify the optimal interventions and their fold changes. The product yield resulting from the identified interventions is evaluated by Flux Balance Analysis using updated steady state conditions. We have applied this method to several test cases. Our results show that SA is capable of identifying several intervention sets with different fold changes values that result in the same yield value of a desired product. Our results also show that predicted maximum yield value increased due to utilizing dynamic constraints that update steady-state bounds.